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Valipour, Fatemeh, Valipour, Zahra, Garousi, Mani, Khadem, Ali. (1405). Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals. سامانه مدیریت نشریات علمی, 16(3), 231-244. doi: 10.31661/jbpe.v0i0.2402-1720
Fatemeh Valipour; Zahra Valipour; Mani Garousi; Ali Khadem. "Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals". سامانه مدیریت نشریات علمی, 16, 3, 1405, 231-244. doi: 10.31661/jbpe.v0i0.2402-1720
Valipour, Fatemeh, Valipour, Zahra, Garousi, Mani, Khadem, Ali. (1405). 'Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals', سامانه مدیریت نشریات علمی, 16(3), pp. 231-244. doi: 10.31661/jbpe.v0i0.2402-1720
Valipour, Fatemeh, Valipour, Zahra, Garousi, Mani, Khadem, Ali. Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals. سامانه مدیریت نشریات علمی, 1405; 16(3): 231-244. doi: 10.31661/jbpe.v0i0.2402-1720

Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals

Journal of Biomedical Physics and Engineering
دوره 16، شماره 3 - شماره پیاپی 1، شهریور 2026، صفحه 231-244    اصل مقاله (999.3 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2402-1720
نویسندگان
Fatemeh Valipour1؛ Zahra Valipour2؛ Mani Garousi3؛ Ali Khadem* 1
1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
3Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
چکیده
Background: Manual analysis of electroencephalography (EEG) for epilepsy diagnosis can be subjective and time-consuming, leading to potential errors. An automatic classification system with high detection accuracy is essential for improving diagnostic efficiency and reliability.
Objective: This study aimed to evaluate a comprehensive set of entropy measures, along with embedding parameters, to identify the most effective single measure for epilepsy diagnosis.
Material and Methods: This analytical study used EEG data from the University of Bonn, including healthy controls (HCs) with open eyes and epileptic seizure patients, each with 100 single-channel segments. Discrete wavelet transform was applied, extracting ten entropy measures and two embedding parameters. Statistical tests evaluated feature significance, and a linear discriminant analysis (LDA) classifier was used for classification. Robustness was assessed by introducing Gaussian noise at varying signal-to-noise ratios (SNRs) and analyzing classification performance.
Results: Our findings indicated that embedding parameters, permutation entropy, fuzzy entropy, sample entropy, norm entropy, sure entropy, log entropy, and threshold entropy significantly differentiated epileptic patients from HCs. Among these, sample entropy, norm entropy, sure entropy, log entropy, threshold entropy, and embedding delay achieved classification accuracies between 97% and 100% using LDA classifier. Furthermore, even with substantial Gaussian noise, the classifier maintained an accuracy above 84%, demonstrating the robustness of these features in noisy conditions. 
Conclusion: This study demonstrated that embedding-based and entropy-based features can serve as effective individual measures for discriminating epileptic EEG signals from HCs. These findings underscore the potential of these measures in automated epilepsy diagnosis systems, resulting in a robust and reliable tool for clinical applications.

تازه های تحقیق

Ali Khadem (Google Scholar)

کلیدواژه‌ها
Epilepsy؛ Electroencephalography (EEG)؛ Entropy؛ Embedding parameters؛ Classification؛ Signal to Noise Ratio (SNR)
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